{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Design a 4 qubit full chip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creates a complete quantum chip and exports it to GDS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell enables [module automatic reload](https://ipython.readthedocs.io/en/stable/config/extensions/autoreload.html?highlight=autoreload). Your notebook will be able to pick up code updates made to the qiskit-metal (or other) module code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import key libraries and open the Metal GUI. Also, we configure the notebook to enable overwriting of existing components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from collections import OrderedDict\n",
    "\n",
    "from qiskit_metal import designs, draw\n",
    "from qiskit_metal import MetalGUI, Dict, Headings\n",
    "\n",
    "design = designs.DesignPlanar()\n",
    "gui = MetalGUI(design)\n",
    "\n",
    "# if you disable the next line, then you will need to delete a component [<component>.delete()] before recreating it\n",
    "design.overwrite_enabled = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import components that will be necessary for the design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_metal.qlibrary.qubits.transmon_pocket_cl import TransmonPocketCL\n",
    "from qiskit_metal.qlibrary.tlines.meandered import RouteMeander\n",
    "from qiskit_metal.qlibrary.tlines.anchored_path import RouteAnchors\n",
    "from qiskit_metal.qlibrary.tlines.pathfinder import RoutePathfinder\n",
    "from qiskit_metal.qlibrary.terminations.launchpad_wb import LaunchpadWirebond\n",
    "from qiskit_metal.qlibrary.terminations.launchpad_wb_coupled import LaunchpadWirebondCoupled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's design the core of the chip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setup the design-wide default settings for trace width and trace gap. These can be customized later for individual transmission lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "design.variables['cpw_width'] = '10 um'\n",
    "design.variables['cpw_gap'] = '6 um'\n",
    "design._chips['main']['size']['size_y'] = '9mm'\n",
    "design._chips['main']['size']['size_y'] = '6.5mm'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need 4 transmons with 3 connection pads each and a chargeline. Let's explore the options of one transmon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pos_x': '0um',\n",
       " 'pos_y': '0um',\n",
       " 'connection_pads': {},\n",
       " '_default_connection_pads': {'pad_gap': '15um',\n",
       "  'pad_width': '125um',\n",
       "  'pad_height': '30um',\n",
       "  'pad_cpw_shift': '5um',\n",
       "  'pad_cpw_extent': '25um',\n",
       "  'cpw_width': 'cpw_width',\n",
       "  'cpw_gap': 'cpw_gap',\n",
       "  'cpw_extend': '100um',\n",
       "  'pocket_extent': '5um',\n",
       "  'pocket_rise': '65um',\n",
       "  'loc_W': '+1',\n",
       "  'loc_H': '+1'},\n",
       " 'chip': 'main',\n",
       " 'pad_gap': '30um',\n",
       " 'inductor_width': '20um',\n",
       " 'pad_width': '455um',\n",
       " 'pad_height': '90um',\n",
       " 'pocket_width': '650um',\n",
       " 'pocket_height': '650um',\n",
       " 'orientation': '0',\n",
       " 'make_CL': True,\n",
       " 'cl_gap': '6um',\n",
       " 'cl_width': '10um',\n",
       " 'cl_length': '20um',\n",
       " 'cl_ground_gap': '6um',\n",
       " 'cl_pocket_edge': '0',\n",
       " 'cl_off_center': '50um',\n",
       " 'hfss_wire_bonds': False,\n",
       " 'q3d_wire_bonds': False,\n",
       " 'hfss_inductance': '10nH',\n",
       " 'hfss_capacitance': 0,\n",
       " 'hfss_resistance': 0,\n",
       " 'hfss_mesh_kw_jj': 7e-06,\n",
       " 'q3d_inductance': '10nH',\n",
       " 'q3d_capacitance': 0,\n",
       " 'q3d_resistance': 0,\n",
       " 'q3d_mesh_kw_jj': 7e-06,\n",
       " 'gds_cell_name': 'my_other_junction'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TransmonPocketCL.get_template_options(design)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to change the `pad_width` for these transmons, as well as define the 3 connection pads and chargeline.\n",
    "\n",
    "To apply the same modifications to all 4 transmons, we define a single option-dictionary to pass to all transmons at the monent of creation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "transmon_options = dict(\n",
    "    connection_pads=dict(\n",
    "        a = dict(loc_W=+1, loc_H=-1, pad_width='70um', cpw_extend = '50um'), \n",
    "        b = dict(loc_W=-1, loc_H=-1, pad_width='125um', cpw_extend = '50um'),\n",
    "        c = dict(loc_W=-1, loc_H=+1, pad_width='110um', cpw_extend = '50um')\n",
    "    ),\n",
    "    gds_cell_name='FakeJunction_01',\n",
    "    cl_off_center = '-50um',\n",
    "    cl_pocket_edge = '180'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create the 4 transmons by specifying the desired coordinates and orientations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "offset_tm = 69  #we the transmon slightly out of center-line\n",
    "\n",
    "q1 = TransmonPocketCL(design, 'Q1', options = dict(\n",
    "    pos_x='+2420um', pos_y=f'{offset_tm}um', **transmon_options))\n",
    "q2 = TransmonPocketCL(design, 'Q2', options = dict(\n",
    "    pos_x='0um', pos_y='-857.6um', orientation = '270', **transmon_options))\n",
    "q3 = TransmonPocketCL(design, 'Q3', options = dict(\n",
    "    pos_x='-2420um', pos_y=f'{offset_tm}um', orientation = '180', **transmon_options))\n",
    "q4 = TransmonPocketCL(design, 'Q4', options = dict(\n",
    "    pos_x='0um', pos_y='+857.6um', orientation = '90', **transmon_options))\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now connect the transmons with transmission lines. We want to have an \"exact length\" transmission line, so we will use the `RouteMeander`. Let's first observe what the default options are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'pin_inputs': {'start_pin': {'component': '', 'pin': ''},\n",
       "  'end_pin': {'component': '', 'pin': ''}},\n",
       " 'fillet': '0',\n",
       " 'lead': {'start_straight': '0mm',\n",
       "  'end_straight': '0mm',\n",
       "  'start_jogged_extension': '',\n",
       "  'end_jogged_extension': ''},\n",
       " 'total_length': '7mm',\n",
       " 'chip': 'main',\n",
       " 'layer': '1',\n",
       " 'trace_width': 'cpw_width',\n",
       " 'meander': {'spacing': '200um', 'asymmetry': '0um'},\n",
       " 'snap': 'true',\n",
       " 'prevent_short_edges': 'true',\n",
       " 'hfss_wire_bonds': False,\n",
       " 'q3d_wire_bonds': False}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RouteMeander.get_template_options(design)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to globally override the default lead (straight initial segment leaving the transmon) and the default fillet (corner rounding radius). Let's collect this information in one dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fillet='99.99um'\n",
    "cpw_options = Dict(\n",
    "    lead=Dict(\n",
    "        start_straight='100um',\n",
    "        end_straight='250um'),\n",
    "    fillet=fillet\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then want each transmission line to be connected to different pins and to have different lengths and asymmetry w.r.t their centerline. Let's collect this information in other dictionaries. Before doing that, to manage the dictionaries in a simpler way, we redefine the `RouteMeander` signature by wrapping it into a convenient method named `connect`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def connect(cpw_name: str, pin1_comp_name: str, pin1_comp_pin: str, pin2_comp_name: str, pin2_comp_pin: str,\n",
    "            length: str, asymmetry='0 um'):\n",
    "    \"\"\"Connect two pins with a CPW.\"\"\"\n",
    "    myoptions = Dict(\n",
    "        pin_inputs=Dict(\n",
    "            start_pin=Dict(\n",
    "                component=pin1_comp_name,\n",
    "                pin=pin1_comp_pin),\n",
    "            end_pin=Dict(\n",
    "                component=pin2_comp_name,\n",
    "                pin=pin2_comp_pin)),\n",
    "        total_length=length)\n",
    "    myoptions.update(cpw_options)\n",
    "    myoptions.meander.asymmetry = asymmetry\n",
    "    return RouteMeander(design, cpw_name, myoptions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now proceed and define the meanders following the signature: `connect(cpw_name, pin1_comp_name, pin1_comp_pin, pin2_comp_name, pin2_comp_pin, length, asymmetry)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "asym = 500\n",
    "cpw1 = connect('cpw1', 'Q1', 'c', 'Q4', 'b', '9000um', f'-{asym-1.25*offset_tm}um')\n",
    "cpw2 = connect('cpw2', 'Q3', 'b', 'Q4', 'c', '9000um', f'+{asym-1.25*offset_tm}um')\n",
    "cpw3 = connect('cpw3', 'Q3', 'c', 'Q2', 'b', '9000um', f'-{asym+0.75*offset_tm}um')\n",
    "cpw4 = connect('cpw4', 'Q1', 'b', 'Q2', 'c', '9000um', f'+{asym+0.75*offset_tm}um')\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's now connect the core elements to the launchpads"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we setup the launchpad location and orientation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# V1 - Corners\n",
    "p1_c = LaunchpadWirebond(design, 'P1_C', options = dict(pos_x='3545um', pos_y='2812um', orientation='270', lead_length='0um'))\n",
    "p2_c = LaunchpadWirebond(design, 'P2_C', options = dict(pos_x='3545um', pos_y='-2812um', orientation='90', lead_length='0um'))\n",
    "p3_c = LaunchpadWirebond(design, 'P3_C', options = dict(pos_x='-3545um', pos_y='-2812um', orientation='90', lead_length='0um'))\n",
    "p4_c = LaunchpadWirebond(design, 'P4_C', options = dict(pos_x='-3545um', pos_y='2812um', orientation='270', lead_length='0um'))\n",
    "\n",
    "# V2\n",
    "p1_q = LaunchpadWirebondCoupled(design, 'P1_Q', options = dict(pos_x='4020um', pos_y='0', orientation='180', lead_length='30um'))\n",
    "p2_q = LaunchpadWirebondCoupled(design, 'P2_Q', options = dict(pos_x='-990um', pos_y='-2812um', orientation='90', lead_length='30um'))\n",
    "p3_q = LaunchpadWirebondCoupled(design, 'P3_Q', options = dict(pos_x='-4020um', pos_y='0', orientation='0', lead_length='30um'))\n",
    "p4_q = LaunchpadWirebondCoupled(design, 'P4_Q', options = dict(pos_x='990um', pos_y='2812um', orientation='270', lead_length='30um'))\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we route. First the V2 launchpads - Exchange Coupler Lines to Edges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "asym = 150\n",
    "cpw_options = Dict(\n",
    "    lead=Dict(\n",
    "        start_straight='430um',\n",
    "        end_straight='0um'),\n",
    "    fillet=fillet\n",
    "    )\n",
    "ol1 = connect('ol1', 'Q1', 'a', 'P1_Q', 'tie', '8.6 mm', f'+{asym}um')\n",
    "ol3 = connect('ol3', 'Q3', 'a', 'P3_Q', 'tie', '8.6 mm', f'+{asym}um')\n",
    "\n",
    "asym = 200\n",
    "cpw_options = Dict(\n",
    "    lead=Dict(\n",
    "        start_straight='535um',\n",
    "        end_straight='0um'),\n",
    "    fillet=fillet\n",
    "    )\n",
    "ol2 = connect('ol2', 'Q2', 'a', 'P2_Q', 'tie', '8.6 mm', f'+{asym}um')\n",
    "ol4 = connect('ol4', 'Q4', 'a', 'P4_Q', 'tie', '8.6 mm', f'+{asym}um')\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we route the V1 launchpads - Chargelines to Corners."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create the transmission lines between the corner launchpads and the qubit charge lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "jogsA_in = OrderedDict()\n",
    "jogsA_in[0] = [\"L\", '200um']\n",
    "\n",
    "options_line_cl1 = {'pin_inputs': \n",
    "            {'start_pin': {'component': 'Q1', 'pin': 'Charge_Line'}, \n",
    "             'end_pin': {'component': 'P1_C', 'pin': 'tie'}},\n",
    "            'lead': {'start_straight': '120um', 'end_straight': '225um','start_jogged_extension': jogsA_in},\n",
    "            'fillet': fillet\n",
    "            }\n",
    "cl1 = RouteAnchors(design, 'line_cl1', options_line_cl1)\n",
    "\n",
    "options_line_cl3 = {'pin_inputs': \n",
    "            {'start_pin': {'component': 'Q3', 'pin': 'Charge_Line'}, \n",
    "             'end_pin': {'component': 'P3_C', 'pin': 'tie'}},\n",
    "            'lead': {'start_straight': '120um', 'end_straight': '225um', 'start_jogged_extension': jogsA_in},\n",
    "            'fillet': fillet\n",
    "            }\n",
    "cl3 = RouteAnchors(design, 'line_cl3', options_line_cl3)\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "jogsB_in = OrderedDict()\n",
    "jogsB_in[0] = [\"L\", '300um']\n",
    "\n",
    "anchors2c = OrderedDict()\n",
    "anchors2c[0] = np.array([2, -2.5])\n",
    "\n",
    "options_line_cl2 = {'pin_inputs': \n",
    "            {'start_pin': {'component': 'Q2', 'pin': 'Charge_Line'}, \n",
    "             'end_pin': {'component': 'P2_C', 'pin': 'tie'}},\n",
    "            'lead': {'start_straight': '200um', 'end_straight': '225um',\n",
    "                     'start_jogged_extension': jogsB_in},\n",
    "            'anchors': anchors2c,\n",
    "            'fillet': fillet\n",
    "            }\n",
    "\n",
    "cl2 = RouteAnchors(design, 'line_cl2', options_line_cl2)\n",
    "\n",
    "anchors4c = OrderedDict()\n",
    "anchors4c[0] = np.array([-2, 2.5])\n",
    "\n",
    "options_line_cl4 = {'pin_inputs': \n",
    "            {'start_pin': {'component': 'Q4', 'pin': 'Charge_Line'}, \n",
    "             'end_pin': {'component': 'P4_C', 'pin': 'tie'}},\n",
    "            'lead': {'start_straight': '200um', 'end_straight': '225um',\n",
    "                     'start_jogged_extension': jogsB_in},\n",
    "            'anchors': anchors4c,\n",
    "            'fillet': fillet\n",
    "            }\n",
    "\n",
    "cl4 = RouteAnchors(design, 'line_cl4', options_line_cl4)\n",
    "\n",
    "gui.rebuild()\n",
    "gui.autoscale()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gui.screenshot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export to GDS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QDesign enables GDS renderer during init. Let's see what the avilable option defaults are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'short_segments_to_not_fillet': 'True',\n",
       " 'check_short_segments_by_scaling_fillet': '2.0',\n",
       " 'gds_unit': 0.001,\n",
       " 'ground_plane': 'True',\n",
       " 'negative_mask': {'main': []},\n",
       " 'corners': 'circular bend',\n",
       " 'tolerance': '0.00001',\n",
       " 'precision': '0.000000001',\n",
       " 'width_LineString': '10um',\n",
       " 'path_filename': '../resources/Fake_Junctions.GDS',\n",
       " 'junction_pad_overlap': '5um',\n",
       " 'max_points': '199',\n",
       " 'cheese': {'datatype': '100',\n",
       "  'shape': '0',\n",
       "  'cheese_0_x': '25um',\n",
       "  'cheese_0_y': '25um',\n",
       "  'cheese_1_radius': '100um',\n",
       "  'view_in_file': {'main': {1: True}},\n",
       "  'delta_x': '100um',\n",
       "  'delta_y': '100um',\n",
       "  'edge_nocheese': '200um'},\n",
       " 'no_cheese': {'datatype': '99',\n",
       "  'buffer': '25um',\n",
       "  'cap_style': '2',\n",
       "  'join_style': '2',\n",
       "  'view_in_file': {'main': {1: True}}},\n",
       " 'bounding_box_scale_x': '1.2',\n",
       " 'bounding_box_scale_y': '1.2'}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_gds = design.renderers.gds\n",
    "\n",
    "a_gds.options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "a_gds.options['no_cheese']['buffer'] = '50um'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correct the path to point to the junction GDS that we intend to include in the output GDS. Then stream out all components in the design."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_gds.options['path_filename'] = '../../resources/Fake_Junctions.GDS'\n",
    "a_gds.export_to_gds(\"Full_chip_design.gds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gui.main_window.close()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
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 "nbformat": 4,
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